Prediction of Breast Cancer using Machine Learning Approaches

Reza Rabiei; Seyed Mohammad Ayyoubzadeh; Solmaz Sohrabei; Marzieh Esmaeili; Alireza Atashi

doi:10.31661/jbpe.v0i0.2109-1403

Prediction of Breast Cancer using Machine Learning Approaches

J Biomed Phys Eng. 2022 Jun 1;12(3):297-308. doi: 10.31661/jbpe.v0i0.2109-1403. eCollection 2022 Jun.

Authors

Reza Rabiei¹, Seyed Mohammad Ayyoubzadeh², Solmaz Sohrabei³, Marzieh Esmaeili², Alireza Atashi⁴

Affiliations

¹ PhD, Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
² PhD, Department of Health Information Technology and Management, School of Allied Medical Sciences, Tehran University of Medical Science, Tehran, Iran.
³ MSc, Department Deputy of Development, Management and Resources, Office of Statistic and Information Technology Management, Zanjan University of Medical Sciences, Zanjan, Iran.
⁴ PhD, Department of E-Health, Virtual School, Tehran University of Medical Sciences, Medical Informatics Research Group, Clinical Research Department, Breast Cancer Research Center, Motamed Cancer Institute, ACECR, Tehran, Iran.

Abstract

Background: Breast cancer is considered one of the most common cancers in women caused by various clinical, lifestyle, social, and economic factors. Machine learning has the potential to predict breast cancer based on features hidden in data.

Objective: This study aimed to predict breast cancer using different machine-learning approaches applying demographic, laboratory, and mammographic data.

Material and methods: In this analytical study, the database, including 5,178 independent records, 25% of which belonged to breast cancer patients with 24 attributes in each record was obtained from Motamed cancer institute (ACECR), Tehran, Iran. The database contained 5,178 independent records, 25% of which belonged to breast cancer patients containing 24 attributes in each record. The random forest (RF), neural network (MLP), gradient boosting trees (GBT), and genetic algorithms (GA) were used in this study. Models were initially trained with demographic and laboratory features (20 features). The models were then trained with all demographic, laboratory, and mammographic features (24 features) to measure the effectiveness of mammography features in predicting breast cancer.

Results: RF presented higher performance compared to other techniques (accuracy 80%, sensitivity 95%, specificity 80%, and the area under the curve (AUC) 0.56). Gradient boosting (AUC=0.59) showed a stronger performance compared to the neural network.

Conclusion: Combining multiple risk factors in modeling for breast cancer prediction could help the early diagnosis of the disease with necessary care plans. Collection, storage, and management of different data and intelligent systems based on multiple factors for predicting breast cancer are effective in disease management.

Keywords: Artificial Intelligence; Breast Cancer; Computing Methodologies; Genetic Algorithm; Machine Learning.